![]() The main scientific developments and the large companies currently working on AI have originated in English-speaking countries and have therefore trained with data in English. Why? Human language is highly varied and complex, it is a living system where the algorithms that weave these digital neurons that make up AI learn from the data that they feed upon, allowing computer cells to acquire vocabulary and improve their linguistic structures thanks to their constant exposure to conversation. The reality of Turing’s endeavor is still very distant today. As early as 1951, the British scientist Alan Turing posed the challenge of the imitation game to test a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. Making machines talk and write is one of the most complex tasks computing has ever faced. Spanish is the world’s second-most spoken language, yet there is no AI capable of processing the Spanish language’s numerous dialectal variants. ![]() In fact, our “conversations” with today’s virtual assistants or chatbots (using voice or text) do not go beyond requesting basic information, giving simple commands, or establishing specific routines. The paradox is that on one hand, we are frightened by a world in which robots could take our jobs, yet at the same time we are unable to communicate for longer than a few minutes with Siri, Alexa, or Google Home. However, beneath that magic that allows computers to behave like the human brain, there lies a combination of technologies and data that go about solving problems in very different ways from the human brain – and these methods sometimes fail. DeepMind beat the world’s Go champion in 2016 and since then a combination of colossal amounts of data, the creation of powerful processing systems (GPUs), and the maturity of neural network algorithms (such as Tensorflow) have turned the machine learning theories developed more than 60 years ago by Marvin Minsky and John McCarthy into a programmable reality. For example, we no longer think twice about smiling in order to unlock our mobile phone, all the while (probably) unaware that during this microsecond, thousands of pixels are being converted into a data feed that the latest deep learning algorithms utilize in order to carry out facial recognition with more than 98% accuracy.ĪI’s rise has been rapid. In fact, AI is fast becoming part of the routine of our daily lives, even if we don’t fully understand exactly what it is. The 2010 Strategic Concept defines NATO’s cores tasks as: collective defence, crisis-management and cooperative security.With each day that passes, we hear more about artificial intelligence. Strategic Concepts lay down the Alliance’s core tasks and principles, its values, the evolving security environment and the Alliance’s strategic objectives for the next decade. It provides a unique link between these two continents, enabling them to consult and cooperate in the field of defence and security, and conduct multinational crisis-management operations together. NATO is an alliance of countries from Europe and North America. These are carried out under the collective defence clause of NATO's founding treaty - Article 5 of the Washington Treaty or under a United Nations mandate, alone or in cooperation with other countries and international organisations. If diplomatic efforts fail, it has the military power to undertake crisis-management operations. MILITARY - NATO is committed to the peaceful resolution of disputes. POLITICAL - NATO promotes democratic values and enables members to consult and cooperate on defence and security-related issues to solve problems, build trust and, in the long run, prevent conflict. NATO’s purpose is to guarantee the freedom and security of its members through political and military means.
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